Artificial Intelligence Predicts Visual Outcomes in Neovascular AMD

Journal Highlights

Schmidt-Erfurth et al. set out to eval­uate the ability of machine learning to predict functional outcomes in patients treated with ranibizumab for neovas­cular age-related macular degeneration (AMD).

They found that, according to their artificial intelligence (AI) algorithms, best-corrected visual acuity (BCVA) at month 3 was the strongest predictive factor of functional outcomes at the 1-year mark. In addition, they found that currently used morphological fea­tures were of limited value in predict­ing BCVA outcome.

For this post hoc analysis of a clinical trial database, the researchers evaluated data from 614 patients who partici­pated in the HARBOR trial. (During HARBOR, patients received intravitreal injections of ranibizumab monthly or on a pro re nata basis for 12 months; in addition, they were evaluated monthly via spectral-domain optical coherence tomography [SD-OCT] imaging.) The researchers used AI algorithms to first correlate OCT parameters observed at baseline to the corresponding visual function at months 1, 2, and 3 and then to predict the patients’ final BCVA at 1 year.

They found that the correlation between predicted and final 12-month BCVA scores was loose at baseline—but by month 3, individual BCVA levels reached a solid predictive power for month 12.

However, fluid-based morphological features proved to be largely irrelevant for predicting therapeutic response, the researchers said.

The latter finding implies that classic exudative features—such as fluid with­in and underneath the retina—may be of limited value in explaining visual function in wet AMD and in providing individual patients with a visual prog­nosis, the authors said, and they added that this should prompt researchers to search for additional markers, such as a disruption of the external limiting membrane.